{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "51decae0-0733-4ab8-b2fb-9c9891ae4b89",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "葡萄酒数据集如下:\n",
      "     label     a1    a2    a3    a4   a5    a6    a7    a8    a9    a10   a11  \\\n",
      "0        1  14.23  1.71  2.43  15.6  127  2.80  3.06  0.28  2.29   5.64  1.04   \n",
      "1        1  13.20  1.78  2.14  11.2  100  2.65  2.76  0.26  1.28   4.38  1.05   \n",
      "2        1  13.16  2.36  2.67  18.6  101  2.80  3.24  0.30  2.81   5.68  1.03   \n",
      "3        1  14.37  1.95  2.50  16.8  113  3.85  3.49  0.24  2.18   7.80  0.86   \n",
      "4        1  13.24  2.59  2.87  21.0  118  2.80  2.69  0.39  1.82   4.32  1.04   \n",
      "..     ...    ...   ...   ...   ...  ...   ...   ...   ...   ...    ...   ...   \n",
      "173      3  13.71  5.65  2.45  20.5   95  1.68  0.61  0.52  1.06   7.70  0.64   \n",
      "174      3  13.40  3.91  2.48  23.0  102  1.80  0.75  0.43  1.41   7.30  0.70   \n",
      "175      3  13.27  4.28  2.26  20.0  120  1.59  0.69  0.43  1.35  10.20  0.59   \n",
      "176      3  13.17  2.59  2.37  20.0  120  1.65  0.68  0.53  1.46   9.30  0.60   \n",
      "177      3  14.13  4.10  2.74  24.5   96  2.05  0.76  0.56  1.35   9.20  0.61   \n",
      "\n",
      "      a12   a13  \n",
      "0    3.92  1065  \n",
      "1    3.40  1050  \n",
      "2    3.17  1185  \n",
      "3    3.45  1480  \n",
      "4    2.93   735  \n",
      "..    ...   ...  \n",
      "173  1.74   740  \n",
      "174  1.56   750  \n",
      "175  1.56   835  \n",
      "176  1.62   840  \n",
      "177  1.60   560  \n",
      "\n",
      "[178 rows x 14 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "names=['label','a1','a2','a3','a4','a5','a6','a7','a8','a9','a10','a11','a12','a13']\n",
    "dataset=pd.read_csv('wine.data',names=names)\n",
    "print('葡萄酒数据集如下:')\n",
    "print(dataset)\n",
    "data=dataset.iloc[range(0,178),range(1,14)]\n",
    "target=dataset.iloc[range(0,178),range(0,1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "a3ae6225-d794-42a9-a242-e69665ff2125",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a 1 中异常值 []\n",
      "a 2 中异常值 [5.8 5.51 5.65]\n",
      "a 3 中异常值 [1.36 3.22 3.23]\n",
      "a 4 中异常值 [10.6 30.0 28.5 28.5]\n",
      "a 5 中异常值 [151.0 139.0 136.0 162.0]\n",
      "a 6 中异常值 []\n",
      "a 7 中异常值 []\n",
      "a 8 中异常值 []\n",
      "a 9 中异常值 [3.28 3.58]\n",
      "a 10 中异常值 [10.8 13.0 11.75 10.68]\n",
      "a 11 中异常值 [1.71]\n",
      "a 12 中异常值 []\n",
      "a 13 中异常值 []\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 15 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['axes.unicode_minus']=False\n",
    "data.plot(kind='box',subplots=True,layout=(3,5),sharex=False,sharey=False)\n",
    "p=data.boxplot(return_type='dict')\n",
    "for i in range(13):\n",
    "    y=p['fliers'][i].get_ydata()\n",
    "    print('a',i+1,'中异常值',y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "bf478d88-8895-4739-b955-6248a67fe4ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.51861254 -0.5622498   0.23205254 ...  0.36217728  1.84791957\n",
      "   1.01300893]\n",
      " [ 0.24628963 -0.49941338 -0.82799632 ...  0.40605066  1.1134493\n",
      "   0.96524152]\n",
      " [ 0.19687903  0.02123125  1.10933436 ...  0.31830389  0.78858745\n",
      "   1.39514818]\n",
      " ...\n",
      " [ 0.33275817  1.74474449 -0.38935541 ... -1.61212515 -1.48544548\n",
      "   0.28057537]\n",
      " [ 0.20923168  0.22769377  0.01273209 ... -1.56825176 -1.40069891\n",
      "   0.29649784]\n",
      " [ 1.39508604  1.58316512  1.36520822 ... -1.52437837 -1.42894777\n",
      "  -0.59516041]]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd \n",
    "from sklearn import preprocessing\n",
    "names=['label','a1','a2','a3','a4','a5','a6','a7','a8','a9','a10','a11','a12','a13']\n",
    "dataset=pd.read_csv('wine.data',names=names)\n",
    "data=dataset.iloc[range(0,178),range(1,14)]\n",
    "target=dataset.iloc[range(0,178),range(0,1)].values.reshape(1,178)[0]\n",
    "cdata=preprocessing.StandardScaler().fit_transform(data)\n",
    "print(cdata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "9dd72c41-29b5-4627-94c9-14929ad6fe54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "x,y=cdata,target\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,random_state=0)\n",
    "k_range=range(1,15)\n",
    "k_error=[]\n",
    "for k in k_range:\n",
    "    model=KNeighborsClassifier(n_neighbors=k)\n",
    "    scores=cross_val_score(model,x,y,cv=5,scoring='accuracy')\n",
    "    k_error.append(1-scores.mean())\n",
    "plt.rcParams['font.sans-serif']='Simhei'\n",
    "plt.plot(k_range,k_error,'r-')\n",
    "plt.xlabel('k的取值')\n",
    "plt.ylabel('预测误差值')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "08186c95-e72c-49e1-b32f-5af0b0d9af04",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型预测准确率 1.0\n",
      "测试集的预测标签 [1 3 2 1 2 2 1 3 2 2 3 3 1 2 3 2 1 1 2 1 2 1 1 2 2 2 2 2 2 3 1 1 2 1 1 1 3\n",
      " 2 2 3 1 1 2 2 2]\n",
      "测试集的真实标签 [1 3 2 1 2 2 1 3 2 2 3 3 1 2 3 2 1 1 2 1 2 1 1 2 2 2 2 2 2 3 1 1 2 1 1 1 3\n",
      " 2 2 3 1 1 2 2 2]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "model=KNeighborsClassifier(n_neighbors=9)\n",
    "model.fit(x_train,y_train)\n",
    "pred=model.predict(x_test)\n",
    "ac=accuracy_score(y_test,pred)\n",
    "print(\"模型预测准确率\",ac)\n",
    "print('测试集的预测标签',pred)\n",
    "print('测试集的真实标签',y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "da86f208-1e72-41ee-a5da-de3013881574",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
